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A statistical method to uncover gene expression changes in spatial transcriptomics

Cell type-specific inference of differential expression (C-SIDE) is a statistical model that identifies which genes (within a determined cell type) are differentially expressed on the basis of spatial position, pathological changes or cell–cell interactions. C-SIDE facilitates differential expression analysis in spatial transcriptomics by jointly modeling cell type mixtures and spatially varying gene expression.

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Fig. 1: C-SIDE accurately learns cell type-specific DE from spatial transcriptomics data.


  1. Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 6434, 1463–1467 (2019). Slide-seq is a high-resolution sequencing-based spatial transcriptomics technology using spatially indexed measurement beads.

    Article  Google Scholar 

  2. Chen, K. H. et al. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348 (2015). MERFISH is an imaging-based spatial transcriptomics technology that profiles gene expression at subcellular resolution.

  3. 10x Genomics. 10x Genomics: Visium spatial gene expression (2020); Visium is a commercially available sequencing-based spatial transcriptomics technology for fixed tissue.

  4. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014). DESeq2 is a statistical method for differential expression in RNA-sequencing.

    Article  Google Scholar 

  5. Cable, D. M. et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat. Biotechnol. 40, 517–526 (2022). RCTD is a statistical method for identifying cell types in spatial transcriptomics, accounting for cell type mixtures.

    Article  CAS  Google Scholar 

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This is a summary of: Cable, D. M. et al. Cell type-specific inference of differential expression in spatial transcriptomics. Nat. Methods (2022).

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A statistical method to uncover gene expression changes in spatial transcriptomics. Nat Methods 19, 1046–1047 (2022).

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